Domain Adaptation Techniques for LLMs: Enhancing Performance in Specialized Fields

Abstract

This paper addresses the challenges and advancements in domain adaptation techniques for Large Language Models (LLMs), particularly focusing on enhancing their performance in specialized fields such as business IT translation. Despite their prowess in general domain tasks, LLMs often fall short in domain-specific applications without tailored adaptation strategies. We evaluate several open-source LLMs, including Llama-2 13B, in both zero-shot and few-shot settings, and explore comprehensive adaptation methods ranging from simple prompting to extensive fine-tuning. Comparisons are drawn against classic neural machine translation models from industrial settings, highlighting the nuances of domain adaptation in bridging the performance gap on domain-specific data. The study also delves into the efficacy of different fine-tuning approaches, providing strategic insights on optimizing training budgets for domain adaptation. Our findings suggest that while LLMs show promising general capabilities, significant adaptation efforts are still necessary to achieve parity with specialized in-domain models.